BiRefNet / config.py
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import os
import math
class Config():
def __init__(self) -> None:
# PATH settings
self.sys_home_dir = os.environ['HOME'] # Make up your file system as: SYS_HOME_DIR/codes/dis/BiRefNet, SYS_HOME_DIR/datasets/dis/xx, SYS_HOME_DIR/weights/xx
# TASK settings
self.task = ['DIS5K', 'COD', 'HRSOD', 'DIS5K+HRSOD+HRS10K', 'P3M-10k'][0]
self.training_set = {
'DIS5K': ['DIS-TR', 'DIS-TR+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4'][0],
'COD': 'TR-COD10K+TR-CAMO',
'HRSOD': ['TR-DUTS', 'TR-HRSOD', 'TR-UHRSD', 'TR-DUTS+TR-HRSOD', 'TR-DUTS+TR-UHRSD', 'TR-HRSOD+TR-UHRSD', 'TR-DUTS+TR-HRSOD+TR-UHRSD'][5],
'DIS5K+HRSOD+HRS10K': 'DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4+DIS-TR+TE-HRS10K+TE-HRSOD+TE-UHRSD+TR-HRS10K+TR-HRSOD+TR-UHRSD', # leave DIS-VD for evaluation.
'P3M-10k': 'TR-P3M-10k',
}[self.task]
self.prompt4loc = ['dense', 'sparse'][0]
# Faster-Training settings
self.load_all = True
self.compile = True # 1. Trigger CPU memory leak in some extend, which is an inherent problem of PyTorch.
# Machines with > 70GB CPU memory can run the whole training on DIS5K with default setting.
# 2. Higher PyTorch version may fix it: https://github.com/pytorch/pytorch/issues/119607.
# 3. But compile in Pytorch > 2.0.1 seems to bring no acceleration for training.
self.precisionHigh = True
# MODEL settings
self.ms_supervision = True
self.out_ref = self.ms_supervision and True
self.dec_ipt = True
self.dec_ipt_split = True
self.cxt_num = [0, 3][1] # multi-scale skip connections from encoder
self.mul_scl_ipt = ['', 'add', 'cat'][2]
self.dec_att = ['', 'ASPP', 'ASPPDeformable'][2]
self.squeeze_block = ['', 'BasicDecBlk_x1', 'ResBlk_x4', 'ASPP_x3', 'ASPPDeformable_x3'][1]
self.dec_blk = ['BasicDecBlk', 'ResBlk', 'HierarAttDecBlk'][0]
# TRAINING settings
self.batch_size = 4
self.IoU_finetune_last_epochs = [
0,
{
'DIS5K': -50,
'COD': -20,
'HRSOD': -20,
'DIS5K+HRSOD+HRS10K': -20,
'P3M-10k': -20,
}[self.task]
][1] # choose 0 to skip
self.lr = (1e-4 if 'DIS5K' in self.task else 1e-5) * math.sqrt(self.batch_size / 4) # DIS needs high lr to converge faster. Adapt the lr linearly
self.size = 1024
self.num_workers = max(4, self.batch_size) # will be decrease to min(it, batch_size) at the initialization of the data_loader
# Backbone settings
self.bb = [
'vgg16', 'vgg16bn', 'resnet50', # 0, 1, 2
'swin_v1_t', 'swin_v1_s', # 3, 4
'swin_v1_b', 'swin_v1_l', # 5-bs9, 6-bs4
'pvt_v2_b0', 'pvt_v2_b1', # 7, 8
'pvt_v2_b2', 'pvt_v2_b5', # 9-bs10, 10-bs5
][6]
self.lateral_channels_in_collection = {
'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
'swin_v1_t': [768, 384, 192, 96], 'swin_v1_s': [768, 384, 192, 96],
'pvt_v2_b0': [256, 160, 64, 32], 'pvt_v2_b1': [512, 320, 128, 64],
}[self.bb]
if self.mul_scl_ipt == 'cat':
self.lateral_channels_in_collection = [channel * 2 for channel in self.lateral_channels_in_collection]
self.cxt = self.lateral_channels_in_collection[1:][::-1][-self.cxt_num:] if self.cxt_num else []
# MODEL settings - inactive
self.lat_blk = ['BasicLatBlk'][0]
self.dec_channels_inter = ['fixed', 'adap'][0]
self.refine = ['', 'itself', 'RefUNet', 'Refiner', 'RefinerPVTInChannels4'][0]
self.progressive_ref = self.refine and True
self.ender = self.progressive_ref and False
self.scale = self.progressive_ref and 2
self.auxiliary_classification = False # Only for DIS5K, where class labels are saved in `dataset.py`.
self.refine_iteration = 1
self.freeze_bb = False
self.model = [
'BiRefNet',
][0]
if self.dec_blk == 'HierarAttDecBlk':
self.batch_size = 2 ** [0, 1, 2, 3, 4][2]
# TRAINING settings - inactive
self.preproc_methods = ['flip', 'enhance', 'rotate', 'pepper', 'crop'][:4]
self.optimizer = ['Adam', 'AdamW'][1]
self.lr_decay_epochs = [1e5] # Set to negative N to decay the lr in the last N-th epoch.
self.lr_decay_rate = 0.5
# Loss
self.lambdas_pix_last = {
# not 0 means opening this loss
# original rate -- 1 : 30 : 1.5 : 0.2, bce x 30
'bce': 30 * 1, # high performance
'iou': 0.5 * 1, # 0 / 255
'iou_patch': 0.5 * 0, # 0 / 255, win_size = (64, 64)
'mse': 150 * 0, # can smooth the saliency map
'triplet': 3 * 0,
'reg': 100 * 0,
'ssim': 10 * 1, # help contours,
'cnt': 5 * 0, # help contours
'structure': 5 * 0, # structure loss from codes of MVANet. A little improvement on DIS-TE[1,2,3], a bit more decrease on DIS-TE4.
}
self.lambdas_cls = {
'ce': 5.0
}
# Adv
self.lambda_adv_g = 10. * 0 # turn to 0 to avoid adv training
self.lambda_adv_d = 3. * (self.lambda_adv_g > 0)
# PATH settings - inactive
self.data_root_dir = os.path.join(self.sys_home_dir, 'datasets/dis')
self.weights_root_dir = os.path.join(self.sys_home_dir, 'weights')
self.weights = {
'pvt_v2_b2': os.path.join(self.weights_root_dir, 'pvt_v2_b2.pth'),
'pvt_v2_b5': os.path.join(self.weights_root_dir, ['pvt_v2_b5.pth', 'pvt_v2_b5_22k.pth'][0]),
'swin_v1_b': os.path.join(self.weights_root_dir, ['swin_base_patch4_window12_384_22kto1k.pth', 'swin_base_patch4_window12_384_22k.pth'][0]),
'swin_v1_l': os.path.join(self.weights_root_dir, ['swin_large_patch4_window12_384_22kto1k.pth', 'swin_large_patch4_window12_384_22k.pth'][0]),
'swin_v1_t': os.path.join(self.weights_root_dir, ['swin_tiny_patch4_window7_224_22kto1k_finetune.pth'][0]),
'swin_v1_s': os.path.join(self.weights_root_dir, ['swin_small_patch4_window7_224_22kto1k_finetune.pth'][0]),
'pvt_v2_b0': os.path.join(self.weights_root_dir, ['pvt_v2_b0.pth'][0]),
'pvt_v2_b1': os.path.join(self.weights_root_dir, ['pvt_v2_b1.pth'][0]),
}
# Callbacks - inactive
self.verbose_eval = True
self.only_S_MAE = False
self.use_fp16 = False # Bugs. It may cause nan in training.
self.SDPA_enabled = False # Bugs. Slower and errors occur in multi-GPUs
# others
self.device = [0, 'cpu'][0] # .to(0) == .to('cuda:0')
self.batch_size_valid = 1
self.rand_seed = 7
run_sh_file = [f for f in os.listdir('.') if 'train.sh' == f] + [os.path.join('..', f) for f in os.listdir('..') if 'train.sh' == f]
with open(run_sh_file[0], 'r') as f:
lines = f.readlines()
self.save_last = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'val_last=' in l][0].split('val_last=')[-1].split()[0])
self.save_step = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'step=' in l][0].split('step=')[-1].split()[0])
self.val_step = [0, self.save_step][0]
def print_task(self) -> None:
# Return task for choosing settings in shell scripts.
print(self.task)
if __name__ == '__main__':
config = Config()
config.print_task()